我正在尝试使用以下R脚本在位于“hdfs：//：/ somnath / merged_train / part-m-00000”的HDFS数据文件上使用RHadoop（rmr2，rhdfs软件包）构建逻辑回归模型，然后进行测试在“hdfs：//：/ somnath / merged_test / part-m-00000”中使用测试HDFS数据文件的模型。
我们正在使用CDH4分发，其中Yarn / MR2与Hadoop-0.20支持的MR1并行运行。并使用hadoop-0.20 mapreduce和hdfs版本以下面显示的Sys.setenv命令运行下面的RHadoop脚本。
但是，每当我运行脚本时，我都会遇到以下错误而几乎没有运气绕过它。如果有人指出这个错误的可能原因，我会很感激，这似乎是由于在没有处理NA参数的情况下在R中进行lapply调用的错误方式。
[root@kkws029 logreg_template]# Rscript logreg_test.R
Loading required package: methods
Loading required package: rJava
HADOOP_CMD=/opt/cloudera/parcels/CDH/lib/hadoop-0.20-mapreduce/bin/hadoop
Be sure to run hdfs.init()
14/08/11 11:59:30 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
NULL
NULL
[1] "Starting to build logistic regression model..."
Error in FUN(X[[2L]], ...) :
Sorry, parameter type `NA' is ambiguous or not supported.
Calls: logistic.regression ... .jrcall -> ._java_valid_objects_list -> lapply -> FUN
Execution halted
以下是我的R脚本：
#!/usr/bin/env Rscript
Sys.setenv(HADOOP_HOME="/opt/cloudera/parcels/CDH/lib/hadoop-0.20-mapreduce")
Sys.setenv(HADOOP_CMD="/opt/cloudera/parcels/CDH/lib/hadoop-0.20-mapreduce/bin/hadoop")
Sys.setenv(HADOOP_BIN="/opt/cloudera/parcels/CDH/lib/hadoop-0.20-mapreduce/bin");
Sys.setenv(HADOOP_CONF_DIR="/opt/cloudera/parcels/CDH/lib/hadoop-0.20-mapreduce/conf");
Sys.setenv(HADOOP_STREAMING="/opt/cloudera/parcels/CDH/lib/hadoop-0.20-mapreduce/contrib/streaming/hadoop-streaming-2.0.0-mr1-cdh4.3.0.jar")
Sys.setenv(LD_LIBRARY_PATH="/usr/lib64/R/library/rJava/jri")
library(rmr2)
library(rhdfs)
.jinit()
.jaddClassPath("/opt/cloudera/parcels/CDH/lib/hadoop/hadoop-auth-2.0.0-cdh4.3.0.jar")
.jaddClassPath("/opt/cloudera/parcels/CDH/lib/hadoop-hdfs/hadoop-hdfs-2.0.0-cdh4.3.0.jar")
.jaddClassPath("/opt/cloudera/parcels/CDH/lib/hadoop/hadoop-common-2.0.0-cdh4.3.0.jar")
hdfs.init()
rmr.options( backend = "hadoop", hdfs.tempdir = "/tmp" )
logistic.regression =
function(hdfsFilePath, iterations, dims, alpha) {
r.file <- hdfs.file(hdfsFilePath,"r")
#hdfsFilePath <- to.dfs(hdfsFilePath)
lr.map =
function(.,M) {
Y = M[,1]
X = M[,-1]
keyval(
1,
Y * X *
g(-Y * as.numeric(X %*% t(plane))))}
lr.reduce =
function(k, Z)
keyval(k, t(as.matrix(apply(Z,2,sum))))
plane = t(rep(0, dims))
g = function(z) 1/(1 + exp(-z))
for (i in 1:iterations) {
gradient =
values(
from.dfs(
mapreduce(
input = as.matrix(hdfs.read.text.file(r.file)),
#input = from.dfs(hdfsFilePath),
map = function(.,M) {
Y = M[,1]
X = M[,-1]
keyval(
1,
Y * X *
g(-Y * as.numeric(X %*% t(plane))))},
reduce = lr.reduce,
combine = T)))
plane = plane + alpha * gradient
#trace(print(plane),quote(browser()))
}
return(plane) }
#validate logistic regression
logistic.regression.test =
function(hdfsFilePath, weight) {
r.file <- hdfs.file(hdfsFilePath,"r")
lr.test.map =
function(.,M) {
keyval(
1,
lapply(as.numeric(M[,-1] %*% t(weight)),function(z) 1/(1 + exp(-z))))}
probabilities =
values(
from.dfs(
mapreduce(
input = as.matrix(hdfs.read.text.file(r.file)),
map = function(.,M) {
keyval(
1,
lapply(as.numeric(M[,-1] %*% t(weight)), function(z) 1/(1 + exp(-z))))}
)))
return(probabilities) }
out = list()
prob = list()
rmr.options( backend = "hadoop", hdfs.tempdir = "/tmp" )
print("Starting to build logistic regression model...")
out[['hadoop']] =
## @knitr logistic.regression-run
logistic.regression(
"hdfs://XX.XX.XX.XX:NNNN/somnath/merged_train/part-m-00000", 5, 5, 0.05)
write.csv(as.vector(out[['hadoop']]), "/root/somnath/logreg_data/weights.csv")
print("Building logistic regression model completed.")
prob[['hadoop']] =
logistic.regression.test(
"hdfs://XX.XX.XX.XX:NNNN/somnath/merged_test/part-m-00000", out[['hadoop']])
write.csv(as.vector(prob[['hadoop']]), "/root/somnath/logreg_data/probabilities.csv")
stopifnot(
isTRUE(all.equal(out[['local']], out[['hadoop']], tolerance = 1E-7)))
注意：我已在root~ / .bash_profile中为HADOOP设置了以下环境变量，如下所示
# Hadoop-specific environment and commands
export HADOOP_HOME=/opt/cloudera/parcels/CDH/lib/hadoop-0.20-mapreduce
export HADOOP2_HOME=/opt/cloudera/parcels/CDH/lib/hadoop
#export HADOOP_CMD=${HADOOP_HOME}/bin/hadoop
#export HADOOP_STREAMING=/opt/cloudera/parcels/CDH/lib/hadoop-mapreduce/hadoop-streaming.jar
#export HADOOP_CONF_DIR=${HADOOP_HOME}/etc/hadoop
export LD_LIBRARY_PATH=${R_HOME}/library/rJava/jri #:${HADOOP_HOME}/../hadoop-0.20-mapreduce/lib/native/Linux-amd64-64
# Add hadoop-common jar to classpath for PlatformName and FsShell classes; Add hadoop-auth and hadoop-hdfs jars
export HADOOP_CLASSPATH=$HADOOP_CLASSPATH:${HADOOP2_HOME}/client-0.20/* #:${HADOOP_HOME}/*.jar:${HADOOP_HOME}/lib/*.jar:${HADOOP2_HOME}/hadoop-common-2.0.0-cdh4.3.0.jar:${HADOOP_HOME}/../hadoop-hdfs/hadoop-hdfs-2.0.0-cdh4.3.0.jar:${HADOOP_HOME}/hadoop-auth-2.0.0-cdh4.3.0.jar:$HADOOP_STREAMING
PATH=$PATH:$R_HOME/bin:$JAVA_HOME/bin:$LD_LIBRARY_PATH:/opt/cloudera/parcels/CDH/lib/mahout:/opt/cloudera/parcels/CDH/lib/hadoop:/opt/cloudera/parcels/CDH/lib/hadoop-hdfs:/opt/cloudera/parcels/CDH/lib/hadoop-mapreduce:/opt/cloudera/parcels/CDH/lib/hadoop-0.20-mapreduce:/var/lib/storm-0.9.0-rc2/lib #:$HADOOP_CMD:$HADOOP_STREAMING:$HADOOP_CONF_DIR
export PATH
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